code: def test_when_eval_metric_is_changed_then_model_can_predict(
      temp_model_path, model_type, eval_metric
  ):
      data = DUMMY_VARIABLE_LENGTH_TS_DATAFRAME.copy()
      model = model_type(
          path=temp_model_path, eval_metric=eval_metric, freq=data.freq
      )
      model.fit(train_data=data)
      predictions = model.predict(data)
      assert (
          len(predictions) == data.num_items * model.prediction_length
      )
  
file_path: autogluon/timeseries/tests/unittests/models/test_mlforecast.py
imports: from unittest import mock
  import numpy
  import pandas
  import pytest
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.models.autogluon_tabular.mlforecast import DirectTabularModel, RecursiveTabularModel
  from autogluon.timeseries.utils.features import TimeSeriesFeatureGenerator
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_ts_dataframe
  from ..common import DATAFRAME_WITH_COVARIATES, DATAFRAME_WITH_STATIC, DUMMY_VARIABLE_LENGTH_TS_DATAFRAME, get_data_frame_with_variable_lengths
label_desc: Use image node_id 3 for calling a global function with example usage: test_when_eval_metric_is_changed_then_model_can_predict(temp_model_path, model_type, eval_metric) without return types